Batch Mode Active Learning and its Applications to Medical Image Classification
Conference Proceeding Article
The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher information. To resolve this challenge, we propose an efficient greedy algorithm that is based on the property of submodular functions. Our empirical studies with five UCI datasets and one real-world medical image classification show that the proposed batch mode active learning algorithm is more effective than the state-of-the-art algorithms for active learning.
Computer Sciences | Databases and Information Systems | Medicine and Health Sciences
Data Management and Analytics
Proceedings of the 23rd International Conference on Machine Learning: Pittsburgh, Pennsylvania: June 25-29, 2006
City or Country
HOI, Steven C. H.; JIN, Rong; ZHU, Jianke; and LYU, Michael R..
Batch Mode Active Learning and its Applications to Medical Image Classification. (2006). Proceedings of the 23rd International Conference on Machine Learning: Pittsburgh, Pennsylvania: June 25-29, 2006. 417-424. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2389